




'''simple docstring'''







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase      ):






 @register_to_config
 def __init__(self       :  List[str]    ,		*,
     a       :  int = 4    ,		a       :  int = 7_68    ,		a       :  int    ,		a       :  List[str]    ,		)       ->   Optional[int]:






    """simple docstring"""


    super().__init__()

    A__       =     nn.Parameter(torch.zeros(a    )    )

    # parameters for additional clip time embeddings
    A__       =     nn.Linear(a    ,		a    )
    A__       =     nn.Linear(a    ,		a    )

    # parameters for encoder hidden states
    A__       =     clip_extra_context_tokens
    A__       =     nn.Linear(
        a    ,		self.clip_extra_context_tokens * cross_attention_dim    )
    A__       =     nn.Linear(a    ,		a    )
    A__       =     nn.LayerNorm(a    )






 def 						_UpperCamelCase (self       :  int    ,		*, a       :  Optional[int]    ,		a       :  Optional[Any]    ,		a       :  Dict    ,		a       :  List[str]    )       ->   Any:






    """simple docstring"""


    if do_classifier_free_guidance:
       # Add the classifier free guidance embeddings to the image embeddings
       A__       =     image_embeddings.shape[0]
       A__       =     self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
       A__       =     classifier_free_guidance_embeddings.expand(
           a    ,		-1    )
       A__       =     torch.cat([classifier_free_guidance_embeddings, image_embeddings]    ,		dim=0    )

    # The image embeddings batch size and the text embeddings batch size are equal
    assert image_embeddings.shape[0] == prompt_embeds.shape[0]

    A__       =     prompt_embeds.shape[0]

    # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
    # adding CLIP embeddings to the existing timestep embedding, ...
    A__       =     self.embedding_proj(a    )
    A__       =     self.clip_image_embeddings_project_to_time_embeddings(a    )
    A__       =     time_projected_image_embeddings + time_projected_prompt_embeds

    # ... and by projecting CLIP embeddings into four
    # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
    A__       =     self.clip_extra_context_tokens_proj(a    )
    A__       =     clip_extra_context_tokens.reshape(a    ,		-1    ,		self.clip_extra_context_tokens    )
    A__       =     clip_extra_context_tokens.permute(0    ,		2    ,		1    )

    A__       =     self.encoder_hidden_states_proj(a    )
    A__       =     self.text_encoder_hidden_states_norm(a    )
    A__       =     torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]    ,		dim=1    )

    return text_encoder_hidden_states, additive_clip_time_embeddings








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_     						=				{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    lowerCAmelCase_     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


